Errors correction with optimised Hopfield neural networks

Abstract

We present in this paper a method of increasing both the storage capacity of Hopfield neural networks and their capability of error correction. The presented method uses the general principles of generating error-correcting codes in information theory combined with a gradient - an heuristic algorithm. Using this method, there are registered improvements in the growth of network storage capacity (number of words memorized), and also in the increase of the correct answers' probability. These types of networks can be used in several applications which use associativity, including the correction errors in communication, the image reconstruction and the object recognition.